INTRODUCTION

Forest bathing (FB) as a medicine have become a topic of increasing importance of scientific and professional discussions across the globe in most recent times (Bach et al., 2021; Clarke, Kotera, & Mcewan, 2021; Farkic, Isailovic, & Taylor, 2021; Mathias, Daigle, Dancause, & Gadais, 2020; Mcewan et al., 2021; Roviello, Gilhen-Baker, Vicidomini, & Roviello, 2021; Seo, Jung, Jeong, Kim, & Choi, 2021; Wen, Yan, Pan, Gu, & Liu, 2019). When Japanese Forestry Agency coined the term “FB or Shinrin-yoku” (absorbing the forest atmosphere) in 1982, the health-improving concept was established as a broad interdisciplinary field between medicine, forestry, and sport tourism (Farkic et al., 2021; Li et al., 2007). Its recommendation to patients is often being considered as more effective for overall healing and wellbeing (Hansen, Jones, & Tocchini, 2017; Li et al., 2007). Those health-improving effects of forests have been suggested to be attributed essentially among other factors to inhalation of biogenic volatile organic compounds (BVOCs) including phytoncides emitted by trees (Li et al., 2007; Roviello et al., 2021). Scientifically proven evidences have shown that exposure to forests can exert specific preventive and therapeutic health benefits such as lowering heart rate and blood pressure, reducing stress hormone production, boosting immunity, improving overall feelings, causing deep sleep and increasing expression of anti-cancer proteins, improving mental wellness and birth delivery, enhancing surgery wound healing, preventing obesity, cardiovascular disease, gallbladder disease, diabetics, and osteoporosis (Frumkin et al., 2017; Han et al., 2016; Kuo, 2015; Lee, Son, Kim, & Lee, 2019; Li et al., 2007; Li, 2019; Wen et al., 2019).

The inclusion of FB into national health care services for preventive and certain mental health cases is well established in Japan, South Korea, China, and Norway (Clarke et al., 2021; Hansen et al., 2017; Li et al., 2007; Miyazaki, 2018; Roviello et al., 2021; Tsunetsugu, Park, & Miyazaki, 2009). Thus, its recommendations as an effective and promising medicine against anxiety, depression, stress, anger, and addiction have been documented (Furuyashiki, Tabuchi, Norikoshi, Kobayashi, & Oriyama, 2019; Kotera & Rhodes, 2020; Wen et al., 2019). The popularity of FB is increasing worldwide and a growing number of expanding studies and commitments are ongoing in the UK (Mcewan et al., 2021), Australia (Lauder, 2019), Germany, Scotland (Farkic et al., 2021), and the US (Forum, 2019) towards the full incorporation of FB into their national health care treatment plans (Mcewan et al., 2021). Increased commitments regarding the training of certified forest therapists and provision of accessible quality forest cover for bathing have been promoted across the globe. Such laudable commitments specifically for public FB at international and national/local levels included; the establishment of the Japanese International Society of Nature and Forest Medicine, the American Association of Nature and Forest Therapy Guides and Programs, Global, the Forest Therapy South Eastern Europe, FB Institute in the UK, The FB Club in the US (Farkic et al., 2021; Forum, 2019). These organisations have played considerable roles in widespread information about the practice of FB at different international forums online. In Africa, the few shreds of evidence of practicing FB found displayed online originated from South Africa (Africa, 2021; Essences, 2021; Forum, 2019).

While online health information has emerged as a leading avenue in health communication globally (Lee et al., 2019; Wang, Shi, & Kong, 2021), the attention has been drawn to the fundamental role of HPs regarding the plan implementation of FB at national levels (Bach et al., 2021; Meyer & Burger-Arndt, 2014). However, our reconnaissance inquiries have shown that virtually nothing is known about FB among the Health professionals (HPs) in Ogun, Oyo, and Rivers States, Nigeria – a country listed among the largest disease burden of the world by WHO (Vanguard, 2018). The global interests now in exploring the overall health-improving effects of forests have come to the fore involving collaborative efforts between medical practitioners and foresters (Bach et al., 2021; Doimo, Masiero, & Gatto, 2020; Meyer et al., 2014) in particular during this evolving COVID-19 pandemic (Roviello et al., 2021). The role of HPs in the communication of FB has become more important worldwide (Beresford-Kroeger, 2019) but has not been explored in Nigeria. FB, being more patient-focused if initiated by HPs can considerably not only reduce the disease burden that continues to pose a major threat to human public health but also help to increase forest cover and its sustainable conservation in Nigeria. In this study, we analyzed the level of HPs awareness, their willingness to acquire, share knowledge about FB, and to prescribe FB in Port Harcourt, Nigeria. The results of this study may significantly improve HPs awareness and their willingness for health communication research/practices about FB as medicine in Nigeria.

METHODS

Study Hospitals

The study was conducted in the University of Port Harcourt Teaching Hospital (UPTH) and Military Hospital (MH), both in Port Harcourt, Nigeria. The distance between the two hospitals is around 21.5km. While the UPTH (04o53´3´´ N and 06o55´43´´ E) is a tertiary health care teaching and research facility for public health care delivery, MH (04o 48´ 53´´ N and 07o 00´ 15´´ E) is an Armed Forces health facility for military personnel and their family members health care services. The UPTH has in its employment about 1387 HPs including consultants, resident doctors, pharmacists, and nurses. The HPs in the service of MH is around 88 including doctors, pharmacists, and nurses. These classes of health personnel are the targeted HPs for the study. Trees are somewhat moderately part of the facilities, many of which are in the form of shade and avenue trees. Terminalia mantaly, Khaya sp., Mangifera indica, Hura crepitans, Polyalthia longifolia, Terminalia catappa, Hura crepitans including Palm family (Roystonea regia and Elaeis guineensis) are dominant tree species within the two facilities. The two hospitals share common climatic conditions; typically tropical moist rainforest experiencing heavy downpour throughout the year but with reduced frequency in the month of December to March. The average minimum and maximum temperature is around 22.54 °C and 31.03 °C, respectively. The relative humidity levels tend to vary from 58.97 to 94.50% and the average annual rainfall is greater than 2000 mm (Uko & Tamunobereton-Ari, 2013).

Sampling design and data collection

Prior to the commencement of the detailed survey in June 2019, several reconnaissance visits were made to the two hospitals, and inputs necessary for designing a reliable data collection method were obtained. In order to reach the broad respondents, a Snowball sampling approach was employed to serve 500 copies of pre-tested questionnaires to participating HPs but 371 representing 74.2% were retrieved and analyzed. Doctors (n = 215), Nurses (n = 103), and Pharmacists (n = 53), of which UPTH was (n, 283), and MH was (n, 88).

Statistical analyses

SPSS version 22 software was implemented for statistical analysis. Descriptive statistics was used to identify the level of awareness about FB, methods of knowledge acquisition, and sharing in medicine. Multinomial regression logistics was performed at p < 0.05 to test the effect of predictor variables on the likelihood of responding yes or maybe compared to No for FB information on the acquisition, sharing, and prescription to patients.

Willingness was evaluated from the respondents with options ranging from Yes, Maybe, to No, which makes up the dependent variable for the model. This type of measurement offers no definite distance between the categories of “No”, “Maybe”, and “Yes”, and as such, multinomial logistic regression was performed to assess the determinants affecting willingness to acquire, share and prescribe as adopted by Shin and Mccann (2018). Thus, the model is specified as;

1
\begin{align*} &\operatorname{Pr}\left(\mathrm{y}_{\mathrm{i}}=\mathrm{j}\right)=\frac{\exp \left(x_{i} \beta_{j}\right)}{\sum_{k=1}^{3} \exp \left(x_{i} \beta_{k}\right)} \\ &\text { Where:i }=1{,\ldots,}\,\mathrm{n} \text { and } \mathrm{j}=1,2,3 \\ &\qquad\quad\mathrm{y}_{\mathrm{i}}=\left\{\begin{array}{l} 1 \text { if respondent chose "Yes" } \\ 2 \text { if respondent chose "Maybe" } \\ 3 \text { if respondent chose "No" } \end{array}\right. \end{align*}

RESULTS

Demographic information of the HPs

As shown in Table 1, the gender ratio between the male and female HPs who participated in the study was almost 1:1. The frequency of the age group of the participants indicated an increasing trend from 21-30 years to the peak at age 31-40 years, and then considerably decreases to age 51-60 years (Table 1). Over two-fifths (44.2%) of the participants reported having an MBBS degree, and almost three-fifths (58.0%) were medical doctors (Table 1). Close to two-fifths (36.9%) of the participants indicated their monthly salary was between ₦ 100,000 – ₦ 200,000, and 73.8% of them indicated they have practiced not more than 10 years (Table 1).

Bodies regulating medical activities, drug approval and active associations of the HPs

The list of bodies regulating medical activities, drug approval, and active associations of the HPs who participated in the study was summarized in Table 2. Based on the computed percentage frequency distribution, the top 3 popular bodies regulating medical activities included: MDCN, NMA, and NMCN. The dominant body responsible for drugs/medicines approval was listed to be NAFDAC. The participants listed NMA, NMCN, and ARD as the top 3 most active professional associations (Table 2).

Table 1

Demographic information of the HPs (N = 371)

Features

variables

Frequency

Percentage

Gender

Male

183

49.3

Female

188

50.7

Age (years)

21-30

102

27.5

31-40

132

35.6

41-50

102

27.5

51-60

35

9.4

Highest Education

MBBS

164

44.2

B.Pharm

53

14.3

Nursing Certificate

45

12.1

BSc. Nursing

58

15.6

Professional Diploma

5

1.3

FWACP

7

1.9

PhD

38

10.2

Post graduate residency

1

0.3

Profession

Medical doctor

215

58.0

Nurse

103

27.8

Pharmacist

53

14.3

Name of Hospital

UPTH

283

76.2

MH

88

23.8

Salary

<N100,000

69

18.6

₦100,000 – ₦200,000

137

36.9

₦201,000 – ₦300,000

68

18.3

₦301,000 – ₦400,000

48

12.9

> ₦400,000

49

13.2

Practice Years

<1year

23

6.2

1-5years

147

39.6

6-10years

127

34.2

11-15years

33

8.9

16-20years

23

6.2

> 20years

18

4.9

[i] MBBS– Bachelor of Medicine and Bachelor of Surgery

[ii] B.Pharm– Bachelor of Pharmacy

[iii] BSc.Nursing – Bachelor of Science in Nursing

[iv] FAWCP– Fellow of West African College of Physicians

Table 2

Bodies regulating medical activities, drug approval and active associations of HPs (N = 371)

Variables

Bodies

Frequency

Percentage (n = 371)

Boards/bodies regulating medical activity

MDCN

194

52.3

NMA

115

31.0

NMCN

105

28.3

PCN

47

12.7

PSN

34

9.2

NANNM

9

2.4

NDA

8

2.2

Boards/bodies approving drugs/medicines

NAFDAC

353

95.1

NDLEA

53

14.3

PCN

43

11.6

PAN

17

4.6

WACS

1

0.3

Associations

NMA

147

39.6

NMCN

95

25.6

ARD

85

22.9

NDA

64

17.3

MDCN

59

15.9

NANNM

49

13.2

PSN

43

11.6

CMDA

35

9.4

AHAP

24

6.5

PANS

17

4.7

PCN

13

3.5

[i] MDCN: Medical and Dental Council of Nigeria; NMA: Nigerian Medical Association; NDA: Nigerian Dental Association; NMCN: Nursing and Midwifery Council of Nigeria; PSN: Pharmaceutical Society of Nigeria; PCN: Pharmacists Council Of Nigeria; NAFDAC: National Agency for Food and Drug Administration Control; NDLEA: National Drug Law Enforcement Agency; PANS: Pharmaceutical Association of Nigerian Students; CMDA: Christian Medical and Dental Association; WACS: West African College of Surgeons; AHAP: Association of Hospital and Administrative Pharmacists of Nigeria; NANNM: National Association of Nigeria Nurses and Midwives

Awareness of health benefits of forest and FB

Of 371 participants, 185 representing 49.9% of the HPs indicated awareness about the health benefits of the forest. The health benefits of forest listed included: research and raw material for drugs development (50.8%); source of herbs (28.1%); protection from ultraviolet rays, and skin cancer (11.9%); and spot for relaxation and stress relief (9.2%). However, only 61 of the participants representing 16.4% of the HPs indicated awareness of FB of which only 17 (4.6%) expressed they have prescribed FB for stress and psychological related illnesses. What FB means by HPs who indicated awareness included: treatment for stress/high blood pressure relief (27.9%); cure for mental illness (21.3%); treating ailments with nature (19.7%); form of diversional therapy (19.7%); Psychological therapy (11.5%). The limitations to FB prescriptions expressed included: insecurity (53.0%); not standardized practice in Nigeria (29.4%); and no quality forest cover around (17.6%). Over three-fifths, (63.9%) of the HPs who were aware indicated they have had knowledge of FB for over 10 years: 24.6% (5-10 years), and 11.5% (less than 5 years).

Knowledge acquisition and sharing on prescription of medicines by HPs

The methods of knowledge acquisition and information sharing on the prescription of medicines by HPs are depicted in Figure 2; Figure 1. The top 3 methods of knowledge acquisition listed included: online/internet search (59.8%); seminar/conference (43.9%); and continuous medical education (43.6%) (Figure 1). Regarding the information-sharing methods, over three-fifths (60.9%) of the HPs listed seminars and conference: presentations and clinical teachings (27.0%), step down to fellow colleagues (20.8%), health education and talks (14.6%), electronic sharing (14.6%), and ward/teaching rounds (6.4%) (Figure 2).

Figure 1

Knowledge acquisition methods among HPs

https://www.nrfhh.com/f/fulltexts/146743/image1_min.jpg
Figure 2

Knowledge sharing methods among HPs

https://www.nrfhh.com/f/fulltexts/146743/image2_min.jpg

Willingness to acquire, share knowledge and prescribe FB by HPs

3-8 reported the willingness to acquire, share, and prescribe FB by HPs.Descriptive statistics of willingness to acquire, share and prescribe FB by HPs indicated a very high proportion (90.0%) of HPs’ willingness to acquire knowledge about FB. However, the proportion of HPs who indicated a willingness to share information about FB, and prescribe FB decreased to 73.3% and 71.2%, respectively (Table 3). Although 40.7% of HPs did not respond, various reasons for their different responses to their willingness to or not to prescribe included: helping to establish awareness about the potency of FB (14.8%); if scientifically proven (14.6%); not an area of specialization (9.4%); help to improve general well being (7.8%); not totally convinced (6.7); insecurity (3.2%); and if patients are willing (2.7%) (Table 3).

From the inferential statistics, only the age category was a significant factor predicting HPs willingness to acquire knowledge about FB. HPs of 20-30 years (-16.244/-16.383), 31-40years (-15.962/-15.722), and 41-50years (-16.816/-16.006) are significantly less likely to acquire information on FB compared with those above 50 years of age (Table 4). As for sharing information about FB, only the category of years of experience was found significant. That is, HPs with <1 years (-17.911), 1-5years (-16.025), 6-10years (-16.513), 11-15years (-16.546) and 16-10years (-16.620) of working experience are significantly less likely to share information on FB compared with those who have worked above 20 years (Table 5). As for prescription, the regression model has no significant prediction of HPs likelihood to prescribe FB with regards to their individual characteristics (Table 6). However, regarding gender specificity analysis, the model significantly predicted female HPs participants’ willingness to prescribe FB for profession, years of practice, income, and age predictors. It estimated that medical doctors (2.013) are more likely to prescribe than pharmacist. It also predicted younger female medical personnel and those with lesser years of experience and more likely not to prescribe than those within the age bracket of 51-60 years and above 20 years of experience. More so, younger females and those with lower income are more likely to consider (Maybe) prescribing FB than their colleagues of 51-60 years of age and above ₦400,000.00 income (Table 7). The model significantly predicted male respondent’s willingness to prescribe FB for only the <1year category of years of practice. It predicted medical personnel with less than 1 year of experience more likely to prescribe FB than those with above 20 years of experience (Table 8).

Table 3

Descriptive statistics of willingness to acquire, share knowledge, and prescribe FB by HPs

Variables

Responses

Frequency

Percentage

Willingness to acquire knowledge

Yes

334

90.0

No

6

1.6

May be

31

8.4

Total

371

100.0

Sharing of information

Yes

272

73.3

No

19

5.1

May be

80

21.6

Total

371

100.0

Willingness to prescribe

Yes

264

71.2

No

55

14.8

May be

52

14.0

Total

371

100.0

Reasons for willingness to or not to prescribe

No response

151

40.7

It will help to establish awareness about the potency of forest bathing

55

14.8

If scientifically proven

54

14.6

Not an area of specialization

35

9.4

Help to improve general wellbeing

29

7.8

Not totally convinced

25

6.7

Insecurity

12

3.2

If patients are willing

10

2.7

Total

371

100.0

Table 4

Multinomial logistic regression results: Effect of predictor variables on the likelihood of responding “Yes” or “Maybe” compared to “No” for FB knowledge acquisition

Personal Characteristics

Yes

Maybe

Estimate

Std. Error

Wald

Estimate

Std. Error

Wald

Profession

Medical doctor

-0.166

1.401

0.014

1.295

1.626

0.634

Nurse

-0.026

1.324

0.000

0.804

1.579

0.259

Pharmacist (base)

0b

-

-

0b

-

-

Practice Years

< 1 year

2.217

1.470

2.275

4.734

0.000

0.000

1 – 5 years

-13.661

4982.676

0.000

-12.751

4982.676

0.000

6 – 10 years

-15.433

4982.676

0.000

-14.804

4982.676

0.000

11 – 15 years

0.875

7011.039

0.000

1.687

7011.039

0.000

16 – 20 years

-16.779

4982.676

0.000

-16.464

4982.676

0.00

> 20 years (base)

0b

-

-

0b

Income

< ₦100,000

-2.138

2.334

0.839

-2.024

2.505

0.653

₦100,000 – ₦200,000

-1.071

1.959

0.299

-0.942

2.080

0.205

₦201,000 – ₦300,000

-0.023

1.861

0.000

-1.084

2.015

0.289

₦301,000 – ₦400,000

15.746

3629.128

0.000

15.947

3629.128

0.000

> ₦400,000 (base)

0b

-

-

0b

-

-

Gender

Female

0.740

0.976

0.575

0.324

1.058

0.094

Male (base)

0b

-

-

0b

-

-

Age

21 – 30 years

-16.244*

1.783

82.972

-16.383*

1.727

89.983

31 – 40 years

-15.962*

1.424

125.561

-15.722*

1.283

150.123

41 – 50 years

-16.816*

0.856

385.814

-16.006*

0.000

0.000

51 – 60 years (base)

0b

-

-

0b

-

-

[i] Notes: Superscripts *indicate statistical significance at 5%, “No” is the base category to which theother groups, “Yes” and “Maybe” are compared.

Table 5

Multinomial logistic regression results: Effect of predictor variables on the likelihood of responding “Yes” or “Maybe” compared to “No” for FB knowledge sharing

Personal Characteristics

Yes

Maybe

Estimate

Std. Error

Wald

Estimate

Std. Error

Wald

Profession

Medical doctor

-1.575

1.202

1.716

-1.739

1.238

1.973

Nurse

-0.942

1.123

0.704

-1.471

1.172

1.577

Pharmacist (base)

0b

-

-

0b

-

-

Practice Years

< 1 year

-17.911*

1.718

108.741

-17.625*

1.686

109.343

1 – 5 years

-16.025*

1.622

97.653

-16.279*

1.540

111.734

6 – 10 years

-16.513*

1.473

125.665

-16.994*

1.361

155.896

11 – 15 years

-16.546*

1.520

118.474

-16.847*

1.416

141.616

16 – 20 years

-16.620*

0.774

461.135

-16.409*

0.000

0.000

> 20 years (base)

0b

-

-

0b

Income

< ₦100,000

-2.547

1.493

2.909

-2.218

1.594

1.938

₦100,000 – ₦200,000

-0.995

1.366

0.531

-0.155

1.437

0.012

₦201,000 – ₦300,000

-0.234

1.353

0.030

0.287

1.417

0.041

₦301,000 – ₦400,000

-0.948

1.243

0.582

-0.473

1.304

0.132

> ₦400,000 (base)

0b

-

-

0b

-

-

Gender

Female

-0.093

0.579

0.026

-0.255

0.617

0.171

Male (base)

0b

-

-

0b

-

-

Age

21 – 30 years

1.056

1.446

0.533

0.712

1.540

0.214

31 – 40 years

0.057

1.290

0.002

0.279

1.371

0.025

41 – 50 years

-0.107

1.188

0.008

0.389

1.254

0.096

51 – 60 years (base)

0b

-

-

0b

-

-

[i] Notes: Superscripts *indicate statistical significance at 5%, “No” is the base category to which theother groups, “Yes” and “Maybe” are compared.

Table 6

Multinomial logistic regression results: Effect of predictor variables on the likelihood of responding “Yes” or “Maybe” compared to “No” for forest FB prescription

Personal Characteristics

Yes

Maybe

Estimate

Std. Error

Wald

Estimate

Std. Error

Wald

Profession

Medical doctor

0.905

0.475

3.630

1.198

0.638

3.530

Nurse

0.696

0.451

2.388

0.423

0.680

0.387

Pharmacist (base)

0b

-

-

0b

-

-

Practice Years

< 1 year

-0.133

1.221

0.012

0.392

1.505

0.068

1 – 5 years

0.568

1.118

0.258

-0.305

1.377

0.049

6 – 10 years

0.790

1.052

0.564

-0.445

1.286

0.120

11 – 15 years

0.502

1.103

0.207

0.063

1.313

0.002

16 – 20 years

0.991

1.159

0.731

-0.298

1.423

0.044

> 20 years (base)

0b

-

-

0b

Income

< ₦100,000

-1.201

0.836

2.062

-1.178

1.192

0.976

₦100,000 – ₦200,000

-0.813

0.768

1.121

0.122

1.021

0.014

₦201,000 – ₦300,000

0.024

0.754

0.001

0.946

0.985

0.922

₦301,000 – ₦400,000

-0.155

0.745

0.043

0.322

0.948

0.116

> ₦400,000 (base)

0b

-

-

0b

-

-

Gender

Female

0.017

0.347

0.002

-0.444

0.457

0.943

Male (base)

0b

-

-

0b

-

-

Age

21 – 30 years

-0.055

1.013

0.003

-0.194

1.219

0.025

31 – 40 years

-0.575

0.966

0.354

-0.423

1.138

0.138

41 – 50 years

-1.331

0.889

2.240

-1.461

1.031

2.006

51 – 60 years (base)

0b

-

-

0b

-

-

[i] Notes: Superscripts *indicate statistical significance at 5%, “No” is the base category to which theother groups, “Yes” and “Maybe” are compared.

Table 7

Multinomial logistic regression results: Effect of predictor variables on the likelihood of responding “Yes” or “Maybe” compered to “No” for FB prescription by female HPs

Personal Characteristics

Yes

Maybe

Estimate

Std. Error

Wald

Estimate

Std. Error

Wald

Profession

Medical doctor

2.013*

0.827

5.925

2.251*

1.094

4.233

Nurse

1.010

0.577

3.059

0.032

0.909

0.001

Pharmacist (base)

0b

-

-

0b

-

-

Practice Years

< 1 year

-17.191*

13017.448

0.000

-34.958

13017.448

0.000

1 – 5 years

-16.256*

13017.448

0.000

-35.983

13017.448

0.000

6 – 10 years

-16.274*

13017.448

0.000

-35.481

13017.448

0.000

11 – 15 years

-16.686*

13017.448

0.000

-34.853

13017.448

0.000

16 – 20 years

-15.972*

13017.448

0.000

-53.136

15475.758

0.000

Above 20 years (base)

0b

-

-

0b

Income

< N100,000

-0.501

1.441

0.121

16.930

1.529*

122.685

N100,000 – N200,000

0.037

1.411

0.001

17.719

1.396*

161.054

N201,000 – N300,000

0.493

1.456

0.114

17.479

1.398*

156.212

N301,000 – N400,000

-1.506

1.359

1.228

16.703

0.000*

0.000

Above N400,000 (base)

0b

-

-

0b

-

-

Age

21 – 30 years

-17.927*

1.776

101.870

-16.670

1.303*

163.681

31 – 40 years

-18.010*

1.740

107.146

-17.016

1.158*

215.977

41 – 50 years

-18.516*

1.581

137.189

-18.488

0.000*

0.000

51 – 60 years (base)

0b

-

-

0b

-

-

[i] Notes: Superscripts *indicate statistical significance at 5%, “No” is the base category to which theother groups, “Yes” and “Maybe” are compared.

Table 8

Multinomial logistic regression results: Effect of predictor variables on the likelihood of responding “Yes” or “Maybe” compered to “No” for FB prescription by male HPs

Personal Characteristics

Yes

Maybe

Estimate

Std. Error

Wald

Estimate

Std. Error

Wald

Profession

Medical doctor

0.520

0.674

0.594

0.584

0.869

0.451

Nurse

0.457

0.879

0.271

1.203

1.185

1.031

Pharmacist (base)

0b

-

-

0b

-

-

Practice Years

< 1 year

19.533*

1.722

128.658

20.000

0.000

0.000

1 – 5 years

0.886

1.470

0.363

0.816

1.756

0.216

6 – 10 years

1.558

1.361

1.311

0.462

1.612

0.082

11 – 15 years

0.325

1.319

0.061

0.229

1.552

0.022

16 – 20 years

1.322

1.387

0.908

0.510

1.630

0.098

Above 20 years (base)

0b

-

-

0b

Income

< N100,000

-1.813

1.356

1.788

-20.402

8590.329

0.000

N100,000 – N200,000

-1.821

1.141

2.546

-0.817

1.345

0.369

N201,000 – N300,000

-0.764

1.048

0.531

0.632

1.232

0.263

N301,000 – N400,000

1.214

1.265

0.920

1.484

1.411

1.107

Above N400,000 (base)

0b

-

-

0b

-

-

Age

21 – 30 years

1.432

1.412

1.029

0.102

1.672

0.004

31 – 40 years

0.006

1.176

0.000

-0.381

1.388

0.075

41 – 50 years

-0.765

1.014

0.569

-1.150

1.180

0.950

51 – 60 years (base)

0b

-

-

0b

-

-

[i] Notes: Superscripts *indicate statistical significance at 5%, “No” is the base category to which theother groups, “Yes” and “Maybe” are compared.

DISCUSSION

HPs are one of the main targets of FB and its prescriptions (Frank, 2021; Meyer et al., 2014). Understanding HPs knowledge about FB can be a pivotal step in initiating the nation’s widespread communication, and commitments towards FB prescription in Nigeria. HPs participants in this study showed a very low level of awareness regarding FB and its prescription. These results confirmed that HPs need to be further informed about FB owing to their key role in the implementation of the medical strategy of the forests. In spite of the reported use of online/internet search as the top source of knowledge acquisition by the participated HPs (Figure 1), knowledge deficiency about FB remained high. The low level of awareness found in this study may be explained by the orchestrated alarms frequently raised by orthodox health professionals about forest-based (herbal) medicines in Nigeria (Aiyeloja, 2019). This could also be due to the communication gap between bodies regulating medical activities, drug approval, and active associations of the HPs. This highlights the importance of regulating bodies’ involvement in collaborative seminars and conferences at the international level, acquiring emerging information that can be considered at the national level for implementation (Ayanbode & Nwagwu, 2021; Meyer et al., 2014). Nevertheless, they indicated more increase in willingness to acquire than sharing knowledge about FB, which may have important health impacts in the long-term prescription of FB. These results are in line with previous studies (Karjalainen, Sarjala, & Raitio, 2010; Sukums et al., 2014).Karjalainen et al. (2010) reported that the lack of health practitioners’ awareness of the potential of forests for improving human health as one of the major challenges. Sukums et al. (2014) reported that most health workers in rural African primary health facilities had little computer knowledge, but had positive attitudes and willingness towards adoption of technology.

Although 90.0% of the participated HPs expressed willingness to acquire more knowledge about FB, the multinomial logistic regression results demonstrated that the willingness to acquire more knowledge about FB was influenced by only demographic aspect of age. HPs of 50 years old and above were significantly indicated willingness to seek more information about FB. This suggests the preparedness of older HPs to update themselves more about FB than younger colleagues. The possible explanation for this may be a national staff’s recruitment/tutorial culture, where younger colleagues are like trainees acquiring practical knowledge and experience from older/senior colleagues (Asemahagn, 2014). Culturally in Nigeria, older people in respective of their professions are known to be vast in knowledge, thus this result implied they are ready to be more abreast of new emerging medicine for younger colleagues to learn from them.

The percentage of the participated HPs willing to share knowledge about FB is similar to the findings ofAsemahagn (2014) who reported 70.0% of the HPs willingness to share knowledge, and experience with their colleagues in hospitals under the Addis Ababa health bureau, Ethiopia. The significance of willingness to share knowledge due to influence of year of experience indicated health professionals of 20 years and above of practice are likely to share more knowledge about FB. The longer and more actively one has practiced within a profession the more likely the experienced is to share knowledge. The fear that sharing knowledge by inexperienced HPs may jeopardize their job security is one probable explanation for this result. As experience and age are intertwined, this result is in agreement with findings ofBalogun (2014) who reported that willingness of health workers to share tacit knowledge increased with age in Nigeria.

As for gender specificity, the statistical analyses demonstrated Female HPs as being more likely to prescribe FB than male counterparts. Previous studies have found that women are more emotional (Fischer, Mosquera, Vianen, & Manstead, 2004), and they significantly communicate more on health-related issues than men (Balogun, 2014; Lemire, Pare, Sicotte, & Harvey, 2008; Nolke, Mensing, Kramer, & Hornberg, 2015). Similarly,Seo et al. (2021) reported that female visitors viewed forest path trekking as relaxation and healing but male visitors viewed forest path trekking as activities, and as such, they tended to likely prescribe FB more than males. A practical explanation for this result is that women are leading in response to health-related issues and caring responsibilities in the family and society than men (Dillip, Mboma, Greer, & Lorenz, 2018; Parker, 2015).

LIMITATIONS AND FUTURE RESEARCH

The present study has some limitations. Firstly, a snowball sampling approach was used to administer questionnaires instead of the face-to-face method. An important demographic factor, marital status which may have impacted positively on willingness results was not included in the analysis due to very low response from the participants. Secondly, the study was conducted in just two hospitals within one out of thirty-six States of Nigeria, thus, the results may not be generalized. Future studies should be conducted nationwide while taking demographic aspect of marital status and other variables such as availability of public internet facilities into considerations. The results of this study will be useful for professionals, academics, and the government to initiate studies on the chemistry of native and introduced trees/shrubs regarding the presence of health-improving BVOCs including phytoncides (Kim, Song, Cho, & Lee, 2020; Li et al., 2007; Wang, 2019) and their potential emission properties in order to select the best candidates for FB gardens and for inclusion in environmental forestry restoration plan. Though health-improving of forests in general have been linked with emissions of chemical compounds in particular phytoncides (Li et al., 2007) but chemicals studies of specific forest species native to North America have revealed Balsam poplar and white pine as outstanding candidates for FB in terms of their quality and quantity releasable BVOCs (Ouellet, Harbilas, Garofalo, Levy, & Haddad, 2016; Toma & Bertman, 2011), and they have been recommended to be planted around hospitals for specific ailments (Beresford-Kroeger, 2019).

CONCLUSIONS

This study has created foreknowledge about FB as a medicine among HPs in Rivers State, and can increase awareness of FB as expressed by participants’ willingness to acquire more and share the knowledge. Age and years of experience were positive predictors for acquiring and sharing knowledge, respectively. However, their willingness to prescribe FB is highly gender sensitive towards female HPs more likely to prescribe than male counterparts. In details, profession, years of practice, income, and age were positive predictors for female. And for male, only years of practice was found as positive predictor. Urgent collaborative researches are needed to specifically test healing effect of some specific forest types or trees. Future conferences and seminars in Nigeria are important featuring professional experts.